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Found 12 papers, 1 papers with code
MultiMediate'23: Engagement Estimation and Bodily Behaviour Recognition in Social Interactions
This paper describes the MultiMediate'23 challenge and presents novel sets of annotations for both tasks.
Multimodal Vision Transformers with Forced Attention for Behavior Analysis
Human behavior understanding requires looking at minute details in the large context of a scene containing multiple input modalities.
Bodily Behaviors in Social Interaction: Novel Annotations and State-of-the-Art Evaluation
Body language is an eye-catching social signal and its automatic analysis can significantly advance artificial intelligence systems to understand and actively participate in social interactions.
Interpretable Gait Recognition by Granger Causality
We apply the graphical Granger model (GGM) to obtain the so-called Granger causal graph among joints as a discriminative and visually interpretable representation of a person's gait.
Multimodal Personality Recognition using Cross-Attention Transformer and Behaviour Encoding
Personality computing and affective computing have gained recent interest in many research areas.
FLAME: Facial Landmark Heatmap Activated Multimodal Gaze Estimation
3D gaze estimation is about predicting the line of sight of a person in 3D space.
How Unique Is a Face: An Investigative Study
Face recognition has been widely accepted as a means of identification in applications ranging from border control to security in the banking sector.
Gait Recognition from Motion Capture Data
This paper contributes to the state-of-the-art with a statistical approach for extracting robust gait features directly from raw data by a modification of Linear Discriminant Analysis with Maximum Margin Criterion.
You Are How You Walk: Uncooperative MoCap Gait Identification for Video Surveillance with Incomplete and Noisy Data
This work offers a design of a video surveillance system based on a soft biometric -- gait identification from MoCap data.
An Evaluation Framework and Database for MoCap-Based Gait Recognition Methods
As a contribution to reproducible research, this paper presents a framework and a database to improve the development, evaluation and comparison of methods for gait recognition from motion capture (MoCap) data.
Walker-Independent Features for Gait Recognition from Motion Capture Data
MoCap-based human identification, as a pattern recognition discipline, can be optimized using a machine learning approach.
Learning Robust Features for Gait Recognition by Maximum Margin Criterion
In the field of gait recognition from motion capture data, designing human-interpretable gait features is a common practice of many fellow researchers.
